A Probabilistic Assessment of Enhanced Geothermal Systems, Uncertainty, and Levelized Cost of Electricity

Abstract

This work uses the coupled performance between an EGS reservoir and a binary power plant to determine the probability of meeting a target levelized cost of electricity (LCOE) as a function of the combined uncertainties in reservoir and power plant performance and capital and operating and maintenance (O&M) costs. Our motivation lies in answering the question, `What combinations of reservoir and power plant performance and capital and O&M costs allow us to hit a target LCOE?`. This question is the inverse to what is usually addressed (What is the LCOE for a given set of inputs?) and allows us to better rank the factors that influence LCOE the most and provide insight with regards to where performance and/or cost cutting efforts should be focused. Using the integrated geothermal systems model, GT-Mod, a hypothetical system is created that uses 2 injection wells, 4 production wells, initial reservoir temperature of 180 oC, and a target output of 25 MW as the basis for the analysis. Geo- and working-fluid mass flow rates are varied accordingly to maintain the 25 MW output. Uncertainty in reservoir drawdown is represented using a function that varies thermal drawdown from the initial temperature to the plant minimum operating temperature over a range of early to late-time decline pathways. Plant performance is varied by randomly adjusting the heat exchanger pinch point value, isentropic efficiency of the turbine and feed-pump, as well as the ambient operating conditions. Default capital and O&M costs are calculated in GETEM and then varied across realistic ranges using uniformly distributed random variables. Capital and O&M costs are refined into four sub-categories each; exploration and confirmation, well costs, plant costs, and makeup and other costs. Results are presented as a set of probability plots that show the performance and economic conditions that must exist to reach different LCOE targets. Tornado plots showing how different uncertainties influence the LCOE and correlation analyses that prioritize and rank the uncertainties are also presented.